{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验七：基于神经网络的手写数字识别\n",
    "\n",
    "### 罗玉轩 20221202433\n",
    "\n",
    "## 一、实验目的\n",
    "\n",
    "1. 使用PyTorch构建两层神经网络\n",
    "2. 在MNIST数据集上训练模型\n",
    "3. 实现测试集准确率95%以上的目标\n",
    "4. 可视化训练过程和预测结果\n",
    "\n",
    "## 二、实验环境\n",
    "\n",
    "- Python 3.9+\n",
    "- PyTorch 1.10+\n",
    "- torchvision\n",
    "- matplotlib\n",
    "- numpy\n",
    "\n",
    "## 三、实验内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入必要的库\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 设置随机种子以保证可重复性\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# 定义神经网络模型\n",
    "class NeuralNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NeuralNet, self).__init__()\n",
    "        self.fc1 = nn.Linear(28*28, 512)  # 输入层到隐藏层\n",
    "        self.fc2 = nn.Linear(512, 10)      # 隐藏层到输出层\n",
    "        self.relu = nn.ReLU()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 28*28)  # 展平图像\n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 404: Not Found\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data\\MNIST\\raw\\train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9912422/9912422 [00:17<00:00, 555289.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\train-images-idx3-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 404: Not Found\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\\train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 28881/28881 [00:00<00:00, 46379.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\train-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 404: Not Found\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data\\MNIST\\raw\\t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1648877/1648877 [00:03<00:00, 435421.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\t10k-images-idx3-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 404: Not Found\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4542/4542 [00:00<00:00, 1079105.52it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz to ./data\\MNIST\\raw\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 加载MNIST数据集\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "train_set = torchvision.datasets.MNIST(\n",
    "    root='./data', \n",
    "    train=True, \n",
    "    download=True, \n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "test_set = torchvision.datasets.MNIST(\n",
    "    root='./data', \n",
    "    train=False, \n",
    "    download=True, \n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    train_set, \n",
    "    batch_size=64, \n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    test_set, \n",
    "    batch_size=64, \n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始化模型、损失函数和优化器\n",
    "model = NeuralNet()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练...\n",
      "Epoch [1/10], Train Loss: 0.3114, Train Acc: 90.56%, Test Loss: 0.1643, Test Acc: 94.85%\n",
      "Epoch [2/10], Train Loss: 0.1413, Train Acc: 95.76%, Test Loss: 0.1092, Test Acc: 96.71%\n",
      "Epoch [3/10], Train Loss: 0.1032, Train Acc: 96.82%, Test Loss: 0.1249, Test Acc: 95.93%\n",
      "Epoch [4/10], Train Loss: 0.0844, Train Acc: 97.32%, Test Loss: 0.0970, Test Acc: 96.86%\n",
      "Epoch [5/10], Train Loss: 0.0703, Train Acc: 97.79%, Test Loss: 0.0860, Test Acc: 97.36%\n",
      "Epoch [6/10], Train Loss: 0.0618, Train Acc: 97.99%, Test Loss: 0.0901, Test Acc: 97.23%\n",
      "Epoch [7/10], Train Loss: 0.0581, Train Acc: 98.11%, Test Loss: 0.0780, Test Acc: 97.58%\n",
      "Epoch [8/10], Train Loss: 0.0498, Train Acc: 98.36%, Test Loss: 0.0776, Test Acc: 97.75%\n",
      "Epoch [9/10], Train Loss: 0.0435, Train Acc: 98.49%, Test Loss: 0.0991, Test Acc: 97.08%\n",
      "Epoch [10/10], Train Loss: 0.0410, Train Acc: 98.64%, Test Loss: 0.0856, Test Acc: 97.58%\n"
     ]
    }
   ],
   "source": [
    "# 训练模型\n",
    "def train_model(model, train_loader, criterion, optimizer, num_epochs=10):\n",
    "    train_losses = []\n",
    "    train_accuracies = []\n",
    "    test_losses = []\n",
    "    test_accuracies = []\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        \n",
    "        for images, labels in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            running_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        train_loss = running_loss / len(train_loader)\n",
    "        train_accuracy = 100 * correct / total\n",
    "        train_losses.append(train_loss)\n",
    "        train_accuracies.append(train_accuracy)\n",
    "        \n",
    "        # 测试集评估\n",
    "        model.eval()\n",
    "        test_loss = 0\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for images, labels in test_loader:\n",
    "                outputs = model(images)\n",
    "                loss = criterion(outputs, labels)\n",
    "                test_loss += loss.item()\n",
    "                _, predicted = torch.max(outputs.data, 1)\n",
    "                total += labels.size(0)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        test_loss = test_loss / len(test_loader)\n",
    "        test_accuracy = 100 * correct / total\n",
    "        test_losses.append(test_loss)\n",
    "        test_accuracies.append(test_accuracy)\n",
    "        \n",
    "        print(f'Epoch [{epoch+1}/{num_epochs}], '\n",
    "              f'Train Loss: {train_loss:.4f}, Train Acc: {train_accuracy:.2f}%, '\n",
    "              f'Test Loss: {test_loss:.4f}, Test Acc: {test_accuracy:.2f}%')\n",
    "    \n",
    "    return train_losses, train_accuracies, test_losses, test_accuracies\n",
    "\n",
    "print(\"开始训练...\")\n",
    "train_losses, train_accuracies, test_losses, test_accuracies = train_model(\n",
    "    model, train_loader, criterion, optimizer, num_epochs=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化训练过程\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 损失曲线\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_losses, label='Train Loss')\n",
    "plt.plot(test_losses, label='Test Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "# 准确率曲线\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_accuracies, label='Train Accuracy')\n",
    "plt.plot(test_accuracies, label='Test Accuracy')\n",
    "plt.title('Training and Test Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化预测结果\n",
    "model.eval()\n",
    "dataiter = iter(test_loader)\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "with torch.no_grad():\n",
    "    outputs = model(images)\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "# 显示图像和预测结果\n",
    "plt.figure(figsize=(10, 4))\n",
    "for i in range(6):\n",
    "    plt.subplot(2, 3, i+1)\n",
    "    img = images[i].numpy().squeeze()\n",
    "    plt.imshow(img, cmap='gray')\n",
    "    plt.title(f'Predicted: {predicted[i]}, Actual: {labels[i]}')\n",
    "    plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、实验总结\n",
    "通过本次实验，我深入理解了全连接神经网络的工作原理和实现方法，掌握了使用PyTorch框架构建、训练和评估神经网络的基本流程。实验过程中，我学会了如何分析训练曲线、调整超参数以及评估模型性能。特别是通过可视化手段，直观地理解了模型的预测行为，这对深度学习模型的调试和优化非常有帮助。"
   ]
  }
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